Aug 12, 2016 · In this work, we propose a Structured Sparsity Learning (SSL) method to regularize the structures (ie, filters, channels, filter shapes, and layer depth) of ...
SSL can: (1) learn a compact structure from a bigger DNN to reduce computation cost; (2) obtain a hardware-friendly structured sparsity of DNN to efficiently ...
In this work, we propose a Structured Sparsity Learning (SSL) method to regularize the structures (ie, filters, channels, filter shapes, and layer depth) of ...
«SSL» re-implements the paper Learning Structured Sparsity in Deep Neural Networks. In addition to the different pruning positions mentioned in the paper ...
This work proposes an algorithm to compress DNNs by jointly optimizing structured sparsity and quantization constraints in a single DNN training framework.
This paper propose a structured sparsity learning approach to simplify or speed-up a learned deep network in order for applications in platforms with limited ...
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To address this, a method called Structured Sparsity Learning (SSL) [11] was Introduced to regularize filters, channels, filter shapes, and depth structures in ...
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May 5, 2023 · Abstract: The main goal of network pruning is imposing sparsity on the neural network by increasing the number of parameters with zero value ...
Dec 25, 2022 · In this paper, we introduce a novel approach for learning structured sparse neural networks, aimed at bridging the DNN hardware deployment challenges.
In this paper, we propose a simple and effective regularization strategy to improve the structured sparsity and structured pruning in DNNs from a new ...